1. Field of the Invention
The present invention relates to a classification technique, and more particularly to a method and apparatus for classifying digital images of objects, such as people.
2. Description of the Related Art
Identification and classification of objects in images is an important application useful in many fields. For example, identification and classification of people in images is important and useful for automatic organization and retrieval of images in photo albums, for security applications, etc. Face recognition has been used to identify people in photographs and digital image data.
Reliable face recognition, however, is difficult to achieve because of variations in image conditions and human imaging. Such variations include: 1) lighting variations, such as indoors vs. outdoor illuminations or back-lit vs. front lit images of people; 2) pose changes, such as frontal view vs. side view of people; 3) poor image quality, such as face out of focus or motion blur in images; 4) different facial expressions, such as open eyes vs. closed eyes, open mouth vs. closed mouth, etc; 5) aging of people; etc.
A few publications have studied human recognition techniques in images. One such technique is described in “Automated Annotation of Human Faces in Family Albums”, by L. Zhang, L. Chen, M. Li, and H. Zhang, in Proc. ACM Multimedia, MM'03, Berkeley, Calif., USA, Nov. 2-8, 2003, which discloses human identification methods. In this publication, facial features and contextual features are used to characterize people in images. In this human identification method, however, the facial features and the contextual features of people are assumed to be independent. This is not an accurate assumption and hampers the effectiveness of using facial features and contextual features to characterize people. Also, integration of facial features and contextual features encounters challenges when any of these features are unreliable or unavailable.
Disclosed embodiments of this application address issues associated with human recognition and classification, by using an adaptive context-aided human classification method and apparatus that can identify people in images when some features of the people in images are unavailable. The method and apparatus perform a principled integration of face and clothes recognition data. The method and apparatus select formulas to combine face and clothes recognition data and obtain overall recognition results for use in the classification of people in images. The formulas are selected depending on the availability of data relating to faces and clothes of people in the images.